Abstract
River discharge is a relevant ingredient of the hydrological cycle for a wide scale of utilizations and evaluation of water assets, plan of water-related designs and flood admonitory and relief plans. The predictive discharge of the basin using the machine learning approaches is therefore significant for managing water resources and the prevention of flooding control. This investigation evaluated the viability of several machine learning methods, M5P tree, Random forest, Regression tree, reduced error pruning tree, Gaussian process and support vector machine, to predict the basin discharge of the Kesinga basin. Various statistical measures, i.e. correlation coefficient, mean absolute error, root mean square error, Willmott’s index, Nash–Sutcliffe efficiency coefficient, Legates and McCabe’s index and normalized root mean square, error were utilized to assess the performance of the developed model. The presentation of random forest and M5P models was found to be the best when compared with the regression tree, reduced error pruning tree, Gaussian process and support vector machine–based models. Overall RF-based model gave the best results among all applied models for predicting water discharge for the Kesinga basin with the coefficient of determination (R2) values of 0.978 and 0.890 for the training and testing stages, respectively. The main significance of soft computing techniques is that they help users solve real-world problems by providing approximate results that conventional and analytical models cannot solve.
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References
Agarwal P, Pal L, Alam MA (2019) Regional scale analysis of hydro-meteorological variables in Kesinga sub-catchment of Mahanadi Basin. India Environmental Earth Sciences 78(15):1–25. https://doi.org/10.1007/s12665-019-8457-z
Aldous D (1993) The continuum random tree III. The Annals of Probability, 248–289. www.jstor.org/stable/2244761
Asokan SM, Dutta D (2008) Analysis of water resources in the Mahanadi River Basin, India under projected climate conditions. Hydrological Processes: an International Journal 22(18):3589–3603. https://doi.org/10.1002/hyp.6962
Bhoria S, Sihag P, Singh B, Ebtehaj I, Bonakdari H (2021) Evaluating Parshall flume aeration with experimental observations and advance soft computing techniques. Neural Comput Appl 33(24):17257–17271. https://doi.org/10.1007/s00521-021-06316-9
Brakenridge GR, Cohen S, Kettner AJ, De Groeve T, Nghiem SV, Syvitski JP, Fekete BM (2012) Calibration of satellite measurements of river discharge using a global hydrology model. J Hydrol 475:123–136. https://doi.org/10.1016/j.jhydrol.2012.09.035
Breiman L (1996) Bagging predictors. Machine Learning 24(2):123–140. https://doi.org/10.1007/BF00058655
Breiman L (1999) Random forests - random features. Technical Report 567. Statistics Department, University of California, Berkeley.
Calmant S, Seyler F (2006) Continental surface waters from satellite altimetry. CR Geosci 338(14–15):1113–1122. https://doi.org/10.1016/j.crte.2006.05.012
Chien H, Yeh PJF, Knouft JH (2013) Modeling the potential impacts of climate changeon streamflow in agricultural watersheds of the Midwestern United States. J Hydrol 491:73–88. https://doi.org/10.1016/j.jhydrol.2013.03.026
Chen W, Pradhan B, Li S, Shahabi H, Rizeei HM, Hou E, Wang S (2019) Novel hybrid integration approach of bagging-based fisher’s linear discriminant function for groundwater potential analysis. Nat Resour Res 28(4):1239–1258. https://doi.org/10.1007/s11053-019-09465-w
Cortes C, Vapnik V (1995) Support-Vector Networks. Machine Learning 20(3):273–297. https://doi.org/10.1007/BF00994018
Destouni G, Jaramillo F, Prieto C (2013) Hydroclimatic shifts driven by human water use for food and energy production. Nat Clim Chang 3(3):213–217. https://doi.org/10.1038/nclimate1719
Garg V, Sambare RS, Thakur PK, Dhote PR, Nikam BR, Aggarwal SP (2022) Improving stream flow estimation by incorporating time delay approach in soft computing models. ISH J Hydraul Eng 28(sup1):57–68. https://doi.org/10.1080/09715010.2019.1676171
Ghorbani MA, Khatibi R, Goel A, FazeliFard MH, Azani A (2016) Modeling river discharge time series using support vector machine and artificial neural networks. Environ Earth Sci 75(8):685. https://doi.org/10.1007/s12665-016-5435-6
Ghosh S, Raje D, Mujumdar PP (2010) Mahanadi streamflow: climate change impact assessment and adaptive strategies. Curr Sci 1084–1091. https://www.jstor.org/stable/24111765
Gosain AK, Rao S, Basuray D (2006) Climate change impact assessment on hydrology of Indian river basins. Curr Sci 346–353. https://www.jstor.org/stable/24091868
He Z, Wen X, Liu H, Du J (2014) A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. J Hydrol 509:379–386. https://doi.org/10.1016/j.jhydrol.2013.11.054
Kalmegh SR (2015) Comparative analysis of weka data mining algorithm random forest, random tree and lad tree for classification of indigenous news data. Int J Emerg Technol Adv Eng 5(1):507–517. https://ijiset.com/vol2/v2s2/IJISET_V2_I2_63.pdf
Li G, Tang Z, Yue S, Zhuang K, Wei H (2001) Sedimentation in the shear front off the Yellow River mouth. Cont Shelf Res 21(6–7):607–625. https://doi.org/10.1016/S0278-4343(00)00097-2
Mersel MK, Smith LC, Andreadis KM, Durand MT (2013) Estimation of river depth from remotely sensed hydraulic relationships. Water Resour Res 49(6):3165–3179. https://doi.org/10.1002/wrcr.20176
Muhammad Adnan R, Yuan X, Kisi O, Yuan Y, Tayyab M, Lei X (2019) Application of soft computing models in streamflow forecasting. In Proceedings of the institution of civil engineers-water management (Vol. 172, No. 3, pp. 123–134). Thomas Telford Ltd. https://doi.org/10.1680/jwama.16.00075
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10(3):282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Pandhiani SM, Sihag P, Shabri AB, Singh B, Pham QB (2020) Time-series prediction of streamflows of Malaysian rivers using data-driven techniques. J Irrig Drain Eng 146(7):04020013. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001463
Quinlan JR (1987) Simplifying decision trees. Int J Man Mach Stud 27(3):221–234. https://doi.org/10.1016/S0020-7373(87)80053-6
Quinlan JR (1992) Learning with continuous classes. In 5th Australian joint conference on artificial intelligence (Vol. 92, pp. 343–348). https://doi.org/10.1142/9789814536271
Raje D, Mujumdar PP (2009) A conditional random field–based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin. Water Resour Res 45(10). https://doi.org/10.1029/2008WR007487
Rao PG (1993) Climatic changes and trends over a major river basin in India. Climate Res 2:215–223
Rao PG (1995) Effect of climate change on streamflows in the Mahanadi river basin. India Water International 20(4):205–212. https://doi.org/10.1080/02508069508686477
Sepahvand A, Singh B, Ghobadi M, Sihag P (2021) Estimation of infiltration rate using data-driven models. Arab J Geosci 14(1):1–11. https://doi.org/10.1007/s12517-020-06245-2
Sihag P, Angelaki A, Chaplot B (2020) Estimation of the recharging rate of groundwater using random forest technique. Appl Water Sci 10(7):1–11. https://doi.org/10.1007/s13201-020-01267-3
Singh A, Singh B, Sihag P (2021) Experimental Investigation and Modeling of Aeration Efficiency at Labyrinth Weirs. J Soft Comput Civ Eng 5(3):15–31. https://doi.org/10.22115/SCCE.2021.284637.1311
Singh B, Ebtehaj I, Sihag P, Bonakdari H (2022) An expert system for predicting the infiltration characteristics. Water Supply 22(3):2847–2862. https://doi.org/10.2166/ws.2021.430
Singh B, Sihag P, Deswal S (2019) Modelling of the impact of water quality on the infiltration rate of the soil. Appl Water Sci 9(1):15. https://doi.org/10.1007/s13201-019-0892-1
Sridharam S, Sahoo A, Samantaray S, Ghose DK (2021) Assessment of Flow Discharge in a River Basin Through CFBPNN, LRNN and CANFIS. In Communication Software and Networks (pp. 765–773). Springer, Singapore. https://doi.org/10.1007/978-981-15-5397-4_78
Stutter M, Baggaley N, Wang C (2021) The utility of spatial data to delineate river riparian functions and management zones: a review. Sci Total Environ 757:143982. https://doi.org/10.1016/j.scitotenv.2020.143982
Sullivan C (2002) Calculating a water poverty index. World Dev 30(7):1195–1210. https://doi.org/10.1016/S0305-750X(02)00035-9
Vörösmarty CJ, Fekete BM, Meybeck M, Lammers RB (2000) Global system of rivers: Its role in organizing continental land mass and defining land-to-ocean linkages. Global Biogeochem Cycles 14(2):599–621. https://doi.org/10.1029/1999GB900092
Willmott CJ (1981) On the validation of models. Phys Geogr 2(2):184–194. https://doi.org/10.1080/02723646.1981.10642213
Zakharova E, Nielsen K, Kamenev G, Kouraev A (2020) River discharge estimation from radar altimetry: assessment of satellite performance, river scales and methods. J Hydrol 583:124561. https://doi.org/10.1016/j.jhydrol.2020.124561
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Nivesh, S., Negi, D., Kashyap, P.S. et al. Prediction of river discharge of Kesinga sub-catchment of Mahanadi basin using machine learning approaches. Arab J Geosci 15, 1369 (2022). https://doi.org/10.1007/s12517-022-10555-y
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DOI: https://doi.org/10.1007/s12517-022-10555-y